Update app.py

app.py

@@ -10,6 +10,7 @@ from langchain_core.runnables import RunnablePassthrough
 from qdrant_client import QdrantClient
 from qdrant_client.http.models import Distance, VectorParams
 from operator import itemgetter
+from langchain_community.embeddings import HuggingFaceEmbeddings  # Add this line
 
 from sentence_transformers import SentenceTransformer
 
@@ -44,21 +45,26 @@ def load_and_process_pdfs(pdf_links):
 def setup_vectorstore():
     LOCATION = ":memory:"
     COLLECTION_NAME = "AI_Ethics_Framework"
-    VECTOR_SIZE = 1536
-
+    
     qdrant_client = QdrantClient(location=LOCATION)
 
+    # Use your SentenceTransformer model for embeddings
+    embeddings = HuggingFaceEmbeddings(model_name="Technocoloredgeek/midterm-finetuned-embedding")
+    
+    # Get the vector size from the embeddings
+    VECTOR_SIZE = len(embeddings.embed_query("test"))
+
     # Create the collection
     qdrant_client.create_collection(
         collection_name=COLLECTION_NAME,
         vectors_config=VectorParams(size=VECTOR_SIZE, distance=Distance.COSINE),
     )
 
-    # Create the vector store
+    # Create the vector store with the new embeddings
     qdrant_vector_store = QdrantVectorStore(
         client=qdrant_client,
         collection_name=COLLECTION_NAME,
-        embedding=OpenAIEmbeddings()
+        embedding=embeddings
     )
 
     # Load and add documents